"Knowledge has become the key economic resource and the dominant—and perhaps even the only—source of competitive advantage."
Peter Drucker wrote those words in the 1990s, long before anyone imagined AI agents that could draft legal contracts, write production code, or analyze market dynamics in seconds. Yet his insight has never been more relevant.
Box CEO Aaron Levie recently published a thesis that every enterprise leader should internalize: We are entering the era of context. And the companies that master context engineering will define the next decade of competitive advantage.
In a world where everyone has access to the same intelligence, differentiation comes from the context you provide.
The Context Problem
AI models are becoming extraordinarily capable. They can pass the bar exam, write sophisticated software, and analyze complex data sets. But here's the critical insight Levie articulates: these models, by default, don't know anything about your particular team or organization.
"In an instant, they might be asked to review a legal contract for Ford, and then in the next second be writing code for a new piece of software at Goldman Sachs," Levie notes. "They are fully general-purpose superintelligence systems that can take on any task for anyone that's asking."
The implication is profound: Your company is getting the same expert lawyer as another company. The same engineer. The same analyst.
So how do you differentiate?
Levie's answer is unequivocal: context.
Context about your products. Your customers. Your markets. Your processes. The tribal knowledge that exists in every organization—developed over years, licensed, or acquired. This proprietary information becomes the force-multiplier that transforms generic AI capabilities into genuine competitive advantage.
What Is Context Engineering?
Context engineering has emerged as a critical discipline for enterprise AI success. Unlike prompt engineering—which focuses on crafting instructions for a single interaction—context engineering is a systems discipline that manages the entire information ecosystem an AI model needs to perform effectively.
As Philipp Schmid explains: "Context engineering is the discipline of designing and building dynamic systems that provide the right information and tools, in the right format, at the right time, to give an LLM everything it needs to accomplish a task."
The shift from prompt engineering to context engineering reflects AI's evolution from chatbots to agents. Simple prompts work for simple questions. But when you're asking an AI agent to autonomously execute complex workflows—managing customer support escalations, processing insurance claims, or coordinating supply chain decisions—the quality of context determines success or failure.
Most agent failures are not model failures. They are context failures.
Levie illustrates the challenge vividly: "Just imagine taking an expert lawyer or engineer that by default knows absolutely nothing about your organization, and you only have a single document's worth of space to describe their entire job, every system they have to leverage, all the data that they are supposed to work with for that particular task, what their objectives are, and so on."
Getting the right information to agents becomes the critical requirement for driving productivity.
The Metcalfe's Law of Data
This context problem isn't new. Organizations have always struggled to fully utilize their accumulated knowledge.
Lew Platt, CEO of HP in the 1990s, famously said: "If HP knew what HP knows, we would be three times more productive."
Companies have always been collections of various forms of context—processes, intellectual property, unique ideas, decision-making patterns, customer information. And companies, as they scale, have only been able to utilize a small portion of this knowledge.
AI agents finally make Platt's vision possible.
Mike Cannon-Brookes of Atlassian frames this as a sort of Metcalfe's law of data: Like the original theory on network effects, the more data you have that agents can access and reason over, the more powerful the overall system becomes.
This creates a new form of competitive advantage. The real estate firm with better insights on market pricing will win more clients. The pharmaceutical company that can leverage reams of research data will develop drugs faster. The marketing agency that can generate campaigns informed by deep customer context will outperform competitors.
Context compounds.
The Hard Problem of Enterprise Context
Getting context to agents at scale is extraordinarily difficult.
Jaya Gupta and Ashu Garg at Foundation Capital point out that many critical decision traces for AI agents to operate on don't exist in any software today. The knowledge is trapped in email threads, Slack conversations, meeting notes, and—most challengingly—people's heads.
Beyond availability, agents need:
- Customer data across every touchpoint
- Enterprise documents spanning decades of institutional knowledge
- Conversation history providing relationship context
- Project timelines and dependencies
- Research data and market intelligence
- Financial records with appropriate access controls
- Codebases with architectural context
- HR information while respecting privacy requirements
And the challenges multiply:
Data access controls: When agents operate autonomously, preventing data leakage becomes critical. An agent helping sales should access different information than one supporting HR.
Governance and compliance: What decisions can agents make autonomously? What requires human approval? Where are the regulatory boundaries?
System integration: Enterprise data lives across dozens of disconnected systems. Making that data available to agents requires integration work that will occupy organizations for years.
As Levie acknowledges: "Designing our systems to get agents access to that data, and ensuring that all of our agents can interoperate on that data is going to be incredibly important... Managing the myriad conflicts that emerge is going to be an ongoing work for the next decade."
The Change Management Reality
Here's perhaps the most underappreciated aspect of the context era: We imagined that AI systems would adapt to how we work. It turns out we will adapt to how they work.
This isn't a failure of AI. It's a recognition of its power—and its limitations.
AI agents are extraordinarily capable when given proper context. But "proper context" means structuring information in ways agents can consume. It means redesigning workflows to support human-AI collaboration. It means building processes that capture knowledge in agent-readable formats.
The core tenet of this change, as Levie frames it: "The user is responsible now for directing and guiding agents on how to do their work, ensuring it gets the right context along the way."
This transforms the nature of work itself.
The New Role: Manager of Agents
If context defines the era, a new role defines the workforce: the individual contributor as manager of agents.
"The individual contributor of today becomes the manager of agents in the future," Levie writes. "Their new responsibilities will be providing the oversight and escalation paths, a meaningful amount of coordination throughout the work that the agents are doing, and shepherding work between the various agents—just like managers of teams in the pre-AI era."
This isn't a distant future. Organizations are already discovering that deploying AI agents requires:
- Defining agent objectives with precision
- Providing appropriate context for each workflow
- Monitoring agent output for quality and accuracy
- Handling escalations when agents hit limits
- Coordinating handoffs between agents and humans
The skills that make someone an effective manager of people—clear communication, goal-setting, quality standards, process design—become equally relevant for managing agents.
What This Means for Enterprise Strategy
Levie's thesis has immediate strategic implications:
1. Context Becomes a Capital Investment
Organizations must treat their knowledge assets with the same rigor they apply to financial assets. This means systematic capture, organization, and governance of enterprise context—not as an IT project, but as a strategic imperative.
2. Data Strategy Precedes AI Strategy
You cannot have an AI strategy without a data strategy. The organizations that invested in data infrastructure, knowledge management, and integration architecture will pull ahead. Those playing catch-up will find the gap widening.
3. Workflow Design Is Competitive Advantage
How you structure work to capture and utilize context becomes a differentiator. Organizations that redesign workflows around human-AI collaboration—rather than layering AI onto existing processes—will extract disproportionate value.
4. Change Management Is the Bottleneck
The limiting factor isn't AI capability. It's organizational readiness to adapt. Companies that invest in helping their people become effective managers of agents will outperform those focused solely on technology deployment.
The Bottom Line
Peter Drucker saw the knowledge economy coming. Aaron Levie sees what comes next: the context economy.
In this new era, every organization has access to the same foundation models. The same AI capabilities. The same potential intelligence. What differs is the context—the proprietary knowledge, processes, and data that make AI genuinely useful for your specific situation.
"The teams and companies that can accumulate and best utilize context will drive the greatest productivity and highest output. Those that don't will find it harder and harder to serve customers competitively."
The race isn't to adopt AI. It's to master context.
At OuterEdge, we help organizations transform their enterprise context into AI competitive advantage. From data strategy to workflow redesign to agent deployment, we work with leadership teams to capture the value that context engineering enables. If you're ready to build your context moat, book a strategy call to discuss your AI transformation.